Knowledge-based dialogue systems with internet retrieval have recently attracted considerable attention from researchers. The dialogue systems overcome a major limitation of traditional knowledge dialogue systems, where the timeliness of knowledge cannot be assured, hence providing greater practical application value. Knowledge-based dialogue systems with internet retrieval can be typically segmented into three tasks: Retrieval Decision, Query Generation, and Response Generation. However, many of studies assumed that all conversations require external knowledge to continue, neglecting the critical step of determining when retrieval is necessary. This assumption often leads to an over-dependence on external knowledge, even when it may not be required. Our work addresses this oversight by employing a single unified model facilitated by prompt and multi-task learning approaches. This model not only decides whether retrieval is necessary but also generates retrieval queries and responses. By integrating these functions, our system leverages the full potential of pre-trained models and reduces the complexity and costs associated with deploying multiple models. We conducted extensive experiments to investigate the mutual enhancement among the three tasks in our system. What is more, the experiment results on the Wizint and Dusinc datasets not only demonstrate that our unified model surpasses the baseline performance for individual tasks, but also reveal that it achieves comparable results when contrasted with SOTA systems that deploy separate, specialized models for each task.

知识对话系统与互联网检索近年来吸引了研究人员的广泛关注。通过使用统一的模型实现检索决策、查询生成和回复生成的三个任务，我们的工作不仅解决了使用外部知识的必要性问题，还节约了多模型部署的复杂性和成本。在Wizint和Dusinc数据集上的实验证明了我们的统一模型在各个任务上的卓越性能，以及与针对每个任务单独部署的SOTA系统相比具有可比性的结果。

UniRQR: 一个统一的模型用于互联网基础知识对话系统中的检索决策、查询和回复生成